In [1]:
import numpy as np 
import pandas as pd
In [2]:
df=pd.read_csv('Diwali Sales Data.csv',encoding='latin1')
In [3]:
df.head()
Out[3]:
User_ID Cust_name Product_ID Gender Age Group Age Marital_Status State Zone Occupation Product_Category Orders Amount Status unnamed1
0 1002903 Sanskriti P00125942 F 26-35 28 0 Maharashtra Western Healthcare Auto 1 23952.0 NaN NaN
1 1000732 Kartik P00110942 F 26-35 35 1 Andhra Pradesh Southern Govt Auto 3 23934.0 NaN NaN
2 1001990 Bindu P00118542 F 26-35 35 1 Uttar Pradesh Central Automobile Auto 3 23924.0 NaN NaN
3 1001425 Sudevi P00237842 M 0-17 16 0 Karnataka Southern Construction Auto 2 23912.0 NaN NaN
4 1000588 Joni P00057942 M 26-35 28 1 Gujarat Western Food Processing Auto 2 23877.0 NaN NaN
In [4]:
df.shape
Out[4]:
(11251, 15)
In [5]:
!pip install -U ydata-profiling

Requirement already satisfied: ydata-profiling in c:\users\91901\documents\anaconda1\lib\site-packages (4.6.1)
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In [6]:
from ydata_profiling import ProfileReport
In [7]:
profile = ProfileReport(df, title="DataSet Profile Report")
In [8]:
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[8]:
























In [9]:



     


profile.to_widgets()



     




















    
    








Render widgets:   0%|          | 0/1 [00:00<?, ?it/s]








    
    








VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…
























In [10]:



     


profile.to_notebook_iframe()



     




















    
    






























In [11]:



     


orgin_df=df[['Gender','Age Group','Age','Marital_Status','State','Zone','Occupation','Orders','Amount']]



     



















In [12]:



     


orgin_df.head()



     




















    
    
Out[12]:











  
    

      
      Gender
      Age Group
      Age
      Marital_Status
      State
      Zone
      Occupation
      Orders
      Amount
    

  
  
    

      0
      F
      26-35
      28
      0
      Maharashtra
      Western
      Healthcare
      1
      23952.0
    

    

      1
      F
      26-35
      35
      1
      Andhra Pradesh
      Southern
      Govt
      3
      23934.0
    

    

      2
      F
      26-35
      35
      1
      Uttar Pradesh
      Central
      Automobile
      3
      23924.0
    

    

      3
      M
      0-17
      16
      0
      Karnataka
      Southern
      Construction
      2
      23912.0
    

    

      4
      M
      26-35
      28
      1
      Gujarat
      Western
      Food Processing
      2
      23877.0
    

  



























In [13]:



     


orgin_df.shape



     




















    
    
Out[13]:








(11251, 9)
























In [14]:



     


orgin_df.isnull().sum()



     




















    
    
Out[14]:








Gender             0
Age Group          0
Age                0
Marital_Status     0
State              0
Zone               0
Occupation         0
Orders             0
Amount            12
dtype: int64
























In [15]:



     


orgin_df.info()



     




















    
    






<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11251 entries, 0 to 11250
Data columns (total 9 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Gender          11251 non-null  object 
 1   Age Group       11251 non-null  object 
 2   Age             11251 non-null  int64  
 3   Marital_Status  11251 non-null  int64  
 4   State           11251 non-null  object 
 5   Zone            11251 non-null  object 
 6   Occupation      11251 non-null  object 
 7   Orders          11251 non-null  int64  
 8   Amount          11239 non-null  float64
dtypes: float64(1), int64(3), object(5)
memory usage: 791.2+ KB
























In [16]:



     


amount_mean=orgin_df['Amount'].mean()



     



















In [18]:



     


orgin_df['Amount']=orgin_df['Amount'].fillna(value=amount_mean)



     




















    
    






C:\Users\91901\AppData\Local\Temp\ipykernel_16992\3564231867.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  orgin_df['Amount']=orgin_df['Amount'].fillna(value=amount_mean)
























In [19]:



     


orgin_df['Amount'].isnull().sum()



     




















    
    
Out[19]:








0
























In [20]:



     


X=orgin_df.drop(columns=['Amount'])
y=orgin_df['Amount']



     



















In [21]:



     


X.head()



     




















    
    
Out[21]:











  
    

      
      Gender
      Age Group
      Age
      Marital_Status
      State
      Zone
      Occupation
      Orders
    

  
  
    

      0
      F
      26-35
      28
      0
      Maharashtra
      Western
      Healthcare
      1
    

    

      1
      F
      26-35
      35
      1
      Andhra Pradesh
      Southern
      Govt
      3
    

    

      2
      F
      26-35
      35
      1
      Uttar Pradesh
      Central
      Automobile
      3
    

    

      3
      M
      0-17
      16
      0
      Karnataka
      Southern
      Construction
      2
    

    

      4
      M
      26-35
      28
      1
      Gujarat
      Western
      Food Processing
      2
    

  



























In [22]:



     


from sklearn.model_selection import train_test_split



     



















In [23]:



     


X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=42)



     



















In [24]:



     


X_train.shape,X_test.shape,y_train.shape,y_test.shape



     




















    
    
Out[24]:








((8438, 8), (2813, 8), (8438,), (2813,))
























In [25]:



     


from sklearn.preprocessing import OneHotEncoder,StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline



     



















In [26]:



     


oht=OneHotEncoder(handle_unknown='ignore',sparse_output=False)
std=StandardScaler()



     



















In [27]:



     


X_train



     




















    
    
Out[27]:











  
    

      
      Gender
      Age Group
      Age
      Marital_Status
      State
      Zone
      Occupation
      Orders
    

  
  
    

      11070
      F
      26-35
      30
      0
      Madhya Pradesh
      Central
      Banking
      3
    

    

      7621
      F
      55+
      73
      1
      Haryana
      Northern
      Healthcare
      4
    

    

      10796
      F
      18-25
      22
      0
      Gujarat
      Western
      Construction
      1
    

    

      10239
      M
      26-35
      29
      0
      Maharashtra
      Western
      Banking
      2
    

    

      5652
      M
      46-50
      46
      0
      Karnataka
      Southern
      Banking
      3
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      5734
      F
      18-25
      20
      0
      Bihar
      Eastern
      Banking
      3
    

    

      5191
      F
      46-50
      47
      0
      Uttarakhand
      Central
      Aviation
      1
    

    

      5390
      F
      46-50
      49
      0
      Kerala
      Southern
      Construction
      1
    

    

      860
      F
      18-25
      19
      1
      Bihar
      Eastern
      Aviation
      2
    

    

      7270
      F
      36-45
      39
      1
      Karnataka
      Southern
      Construction
      3
    

  



8438 rows × 8 columns


























In [28]:



     


X_test



     




















    
    
Out[28]:











  
    

      
      Gender
      Age Group
      Age
      Marital_Status
      State
      Zone
      Occupation
      Orders
    

  
  
    

      5749
      M
      26-35
      30
      0
      Haryana
      Northern
      Banking
      4
    

    

      7335
      F
      26-35
      31
      0
      Uttar Pradesh
      Central
      Retail
      2
    

    

      5932
      M
      26-35
      28
      1
      Uttarakhand
      Central
      Automobile
      3
    

    

      3819
      F
      18-25
      22
      0
      Karnataka
      Southern
      Chemical
      2
    

    

      6395
      F
      18-25
      22
      0
      Rajasthan
      Northern
      Food Processing
      4
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      9112
      M
      18-25
      22
      1
      Haryana
      Northern
      IT Sector
      3
    

    

      2269
      M
      36-45
      44
      0
      Andhra Pradesh
      Southern
      Banking
      4
    

    

      9030
      M
      46-50
      46
      0
      Bihar
      Eastern
      Hospitality
      1
    

    

      9966
      F
      26-35
      32
      1
      Karnataka
      Southern
      Healthcare
      3
    

    

      7528
      M
      36-45
      42
      1
      Maharashtra
      Western
      Banking
      1
    

  



2813 rows × 8 columns


























In [29]:



     


X_train_scaled=oht.fit_transform(X_train)
X_test_scaled=oht.transform(X_test)



     



















In [30]:



     


X_train_scaled



     




















    
    
Out[30]:








array([[1., 0., 0., ..., 0., 1., 0.],
       [1., 0., 0., ..., 0., 0., 1.],
       [1., 0., 0., ..., 0., 0., 0.],
       ...,
       [1., 0., 0., ..., 0., 0., 0.],
       [1., 0., 0., ..., 1., 0., 0.],
       [1., 0., 0., ..., 0., 1., 0.]])
























In [31]:



     


X_test_scaled



     




















    
    
Out[31]:








array([[0., 1., 0., ..., 0., 0., 1.],
       [1., 0., 0., ..., 1., 0., 0.],
       [0., 1., 0., ..., 0., 1., 0.],
       ...,
       [0., 1., 0., ..., 0., 0., 0.],
       [1., 0., 0., ..., 0., 1., 0.],
       [0., 1., 0., ..., 0., 0., 0.]])
























In [32]:



     


from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor



     



















In [33]:



     


svr=SVR()
dtr=DecisionTreeRegressor()
knr=KNeighborsRegressor()



     



















In [36]:



     


svr.fit(X_train_scaled,y_train)



     




















    
    
Out[36]:







SVR()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVR()
























In [37]:



     


dtr.fit(X_train_scaled,y_train)



     




















    
    
Out[37]:







DecisionTreeRegressor()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeRegressor()
























In [38]:



     


knr.fit(X_train_scaled,y_train)



     




















    
    
Out[38]:







KNeighborsRegressor()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsRegressor()
























In [39]:



     


y_pred_svr=svr.predict(X_test_scaled)



     



















In [40]:



     


y_pred_dtr=dtr.predict(X_test_scaled)



     



















In [41]:



     


y_pred_knr=knr.predict(X_test_scaled)



     



















In [42]:



     


y_pred_svr



     




















    
    
Out[42]:








array([8089.09506714, 8130.44530743, 8096.58316785, ..., 8099.84763544,
       8119.37959829, 8103.41641902])
























In [43]:



     


y_pred_dtr



     




















    
    
Out[43]:








array([ 7972., 11616.,  2002., ..., 19515.,  8535.,  1681.])
























In [44]:



     


y_pred_knr



     




















    
    
Out[44]:








array([ 7493.8, 11585.8,  8026. , ..., 10420.8, 11178.8,  7372. ])
























In [45]:



     


from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score



     



















In [46]:



     


print("Mean absolute error:- ",mean_absolute_error(y_test,y_pred_svr))
print("Mean squared error:- ",mean_squared_error(y_test,y_pred_svr))
print("Root Mean squared error:- ",np.sqrt(mean_absolute_error(y_test,y_pred_svr)))



     




















    
    






Mean absolute error:-  4193.6740966798
Mean squared error:-  29459301.56008552
Root Mean squared error:-  64.75858318925607
























In [48]:



     


print("Mean absolute error:- ",mean_absolute_error(y_test,y_pred_dtr))
print("Mean squared error:- ",mean_squared_error(y_test,y_pred_dtr))
print("Root Mean squared error:- ",np.sqrt(mean_absolute_error(y_test,y_pred_dtr)))



     




















    
    






Mean absolute error:-  5654.507289388122
Mean squared error:-  51284745.25594983
Root Mean squared error:-  75.19645795772645
























In [47]:



     


print("Mean absolute error:- ",mean_absolute_error(y_test,y_pred_knr))
print("Mean squared error:- ",mean_squared_error(y_test,y_pred_knr))
print("Root Mean squared error:- ",np.sqrt(mean_absolute_error(y_test,y_pred_knr)))



     




















    
    






Mean absolute error:-  4519.1313660053975
Mean squared error:-  31316789.272688925
Root Mean squared error:-  67.224484869766
























In [49]:



     


print("Score :- ",r2_score(y_test,y_pred_svr))
print("Score :- ",r2_score(y_test,y_pred_dtr))
print("Score :- ",r2_score(y_test,y_pred_knr))



     




















    
    






Score :-  -0.06779897129072521
Score :-  -0.8588966922897798
Score :-  -0.13512655082134173
























In [50]:



     


test=pd.DataFrame([['M','46-50',50,0,'Uttar Pradesh','Central','Healthcare',3]],columns=['Gender','Age Group','Age','Marital_Status','State','Zone','Occupation','Orders'])



     



















In [51]:



     


test



     




















    
    
Out[51]:











  
    

      
      Gender
      Age Group
      Age
      Marital_Status
      State
      Zone
      Occupation
      Orders
    

  
  
    

      0
      M
      46-50
      50
      0
      Uttar Pradesh
      Central
      Healthcare
      3
    

  



























In [52]:



     


test_scaled=oht.transform(test)



     



















In [53]:



     


svr.predict(test_scaled)



     




















    
    
Out[53]:








array([8115.10030671])
























In [54]:



     


dtr.predict(test_scaled)



     




















    
    
Out[54]:








array([4536.])
























In [55]:



     


knr.predict(test_scaled)



     




















    
    
Out[55]:








array([6332.2])
























In [ ]: